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血细胞指数与类风湿性关节炎的严重程度的关联:一个回顾性病例控制和机器学习建模研究
Rongqing He1, Boli Qin1, Xiaopeng Qin1
1The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China.
血细胞指数与类风湿性关节炎 (RA) 严重程度有显著的相关性. 机器学习模型,特别是随机森林,准确预测RA疾病活动,帮助RA患者个性化治疗策略.
科学领域:
- 类风湿病学 类风湿病学
- 医疗信息学 医疗信息学
- 血液学 血液学 血液学
背景情况:
- 风湿性关节炎 (RA) 涉及免疫细胞,但其他血细胞指数和疾病活动之间的联系仍然不清楚.
- 了解这些联系对于制定有效的RA管理策略至关重要.
研究的目的:
- 开发一种可解释的机器学习模型,使用血细胞参数来评估RA疾病的严重程度.
- 帮助风湿性关节炎患者个性化治疗决策.
主要方法:
- 一项回顾性病例控制研究分析了来自4401名患者的数据.
- 通过消除递归特征,确定了关键变量;对10个机器学习算法进行了基准测试.
- SHAP评估了模型的解释性;逻辑回归,受限制的立方线和门德尔随机化探索了关联和因果关系.
主要成果:
- 血细胞指数被确定为与RA严重程度相关的主要因素.
- 随机森林模型实现了高预测性能 (AUC为0.870和0.874).
- 门德尔的随机化支持血细胞指数和RA风险之间的因果关系.
结论:
- 血细胞指数与类风湿性关节炎的严重程度密切相关.
- 开发的机器学习模型显示了RA严重程度的预测能力.
- 这种方法可能有助于临床医生定制针对RA的个性化治疗计划.
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